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Particle Filters for Random Set Models || Sensor Control for Random Set BasedParticle Filters

✍ Scribed by Ristic, Branko

Book ID
120178718
Publisher
Springer New York
Year
2013
Tongue
English
Weight
802 KB
Edition
2013
Category
Article
ISBN
1461463165

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✦ Synopsis

This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based  on the Monte Carlo statistical method. Although the resulting  algorithms, known as particle filters, have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.

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